Video recommendation method and device, electronic equipment and storage medium
A video recommendation and video technology, which is applied in digital data processing, machine learning, instruments, etc., can solve the problems of not making good use of user feature information and reducing the effect of video recommendation for old users.
Active Publication Date: 2021-11-16
BEIJING DAJIA INTERNET INFORMATION TECH CO LTD
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[0004] However, in the existing technology, if the deep learning model obtained by reducing the weight of user characteristic information is used, when recommending videos to old users with relatively comprehensive user characteristic information, the user characteristic information cannot be well utilized, which will reduce Video recommendation effect of old users
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[0090] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.
[0091] figure 1 is a schematic structural diagram of a set of video recommendation models according to an exemplary embodiment, as shown in figure 1 As shown, a video recommendation method of the present disclosure is applied to a video recommendation model set including an old user prediction model 10 , a user maturity prediction model 20 and a new user prediction model 30 .
[0092]A...
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The invention provides a video recommendation method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining behavior feature information of a target object and video feature information of a target video; inputting the behavior characteristic information into a user maturity prediction model to obtain the maturity of the target object; determining a first prediction score according to the behavior feature information and the video feature information; determining a second prediction score according to the video feature information; and determining the score of the target video according to the first prediction score and the second prediction score. In the application, the integrity of the behavior characteristic information is evaluated by using the maturity of the target object obtained by the user maturity prediction model, so that the maturity, the first prediction score considering the behavior characteristic information is considered, and the second prediction score not considering the behavior characteristic information of the object can be integrated; a video recommendation method capable of comprehensively evaluating a complete group including new users and old users is obtained, so that a better video recommendation effect can be obtained for the users of the complete group.
Description
technical field [0001] The present application relates to the technical field of big data recommendation, and in particular to a video recommendation method, device, electronic equipment and storage medium. Background technique [0002] With the development of deep learning technology, it is possible to learn the user's interests and preferences based on the user's attributes and historical behavior records, and filter out the parts that the user may be interested in from the massive content and recommend them to the user to solve the problem in the era of big data. problem of information overload. [0003] However, when recommending information for new users, due to the lack of user characteristic information of new users, deep learning technology needs to face the problem of cold start with less user characteristic information, that is, when there is little or no user characteristic information Predicting the relevant results under certain circumstances makes the predicti...
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CPCG06F16/735G06F16/783G06N20/00
Inventor 戴尚峰赵致辰杨森刘霁
Owner BEIJING DAJIA INTERNET INFORMATION TECH CO LTD